Unsupervised learning in automatic sarcasm identification is still in its infancy, and most approaches are clustering-based, which are mostly applicable to pattern recognition. Nudged by the limitations and difficulties inherent in labeling the datasets (i.e., time- and labor-intensivity) in supervised learning methods, researchers seek to eliminate such exertions by focusing on the development of unsupervised models. [1]} propose an unsupervised framework for domain-independent irony detection. They build on probabilistic topic models originally defined for sentiment analysis. These models are extensions of the well-known Latent Dirichlet Allocation (LDA) model [2]}. They propose Topic-Irony model (TIM), which is able to model irony toward different topics in a fully unsupervised setting, enabling each word in a sentence to be generated from the same irony-topic distribution. They enrich their model with a neural language lexicon derived through word embeddings. In a similar attempt, [3]} utilize both supervised and unsupervised settings. They use Naïve Bayes for supervised and Fuzzy C-means (FCM) clustering for unsupervised learning. Justifiably, FCM does not perform as effectively as NB.
